针对传统油中溶解气体分析（dissolved gas analysis，DGA）在油浸变压器故障诊断过程中不能够有效地利用故障信息，以及变压器故障样本类型不平衡致使模型诊断结果较差的情况，提出了基于数据扩充和故障特征优化的SCNGO-SVM-AdaBoost变压器故障诊断技术。首先，针对不平衡样本数据集利用安全级别合成少数过采样技术（safe-level synthetic minority over-sampling technique，safe-level SMOTE）对原始的变压器故障样本集进行了数据扩充，然后利用核主成分分析（kernel principal component analysis，K-PCA）算法对比值化后的油色谱数据进行故障特征优化提取。其次在北方苍鹰优化算法（northern goshawk optimization，NGO）中融合了正余弦和折射反向学习策略，利用测试函数验证该算法的稳定性和利用SCNGO优化算法提高其寻优能力。最后通过实际的对未扩充样本诊断和其他方法诊断进行对比分析，结果证明该方法能够有效地提高变压器故障诊断的性能。

The traditional dissolved gas analysis （DGA） in oil-immersed transformer fault diagnosis is unable to effectively utilize fault information, and the imbalance of fault sample types results in poor diagnostic model performance. To address this, a transformer fault diagnosis technique based on data augmentation and fault feature optimization, named SCNGO-SVM-AdaBoost, is proposed. Firstly, to handle the imbalanced sample dataset, the safe-level synthetic minority over-sampling technique （safe-level SMOTE） is used for data augmentation of the original transformer fault sample set. Then, kernel principal component analysis （K-PCA） is employed to optimize and extract fault features from the ratio-based oil chromatographic data. Secondly, the northern Goshawk optimization （NGO） algorithm is enhanced by integrating positive and negative cosine and refraction reverse learning strategies. The stability of this algorithm is verified using test functions and optimization capability is improved by the diagnostic algorithm. Through a comparative analysis of actual fault diagnosis using the original samples and other methods, the results show that this approach can effectively improve the performance of transformer fault diagnosis.